The Faculty of Informatics of MU brings together the DISA, Sitola and NLP expert groups in this project under the leadership of the Service Systems Laboratory (SeSLab). Laboratory of Service Systems has been conducting research into Smart Cities since 2018. In terms of practical application, it collaborates on the preparation of implementation materials for the SMART concept and cooperates in research activities in this area with the University of Salerno in Italy, the Polytechnic University of Bucharest, and the Deggendorf Institute of Technology. A very important partner in the field of AI application research in services is ISSIP, led by Jim Spohrer, one of the founders of Service Science, which forms the theoretical and methodological framework for the entire research project. The Faculty of Informatics, through Leonard Walletzky and Jaroslav Zelený, collaborated on the SMOČR project "Strategic Framework of the Union of Towns and Municipalities in the Area of Smart City." The aforementioned employees are the authors of the chapter on ICT and also the appendix on ICT methodology for Smart City MMR in 2018.
Group CODA from the DISA laboratory draws on more than twenty years of experience in processing and searching complex data. In its current collaboration with the University of Kiel on the GAČR project "Learned Indexes for Similarity Searching," it is researching and applying a new type of data structure that uses machine learning models to accelerate searches in documents, images, and proteins.
The Sitola Laboratory has many years of experience in planning using artificial intelligence and operational research methods, the foundations of which were laid when the Faculty of Informatics was established. Group Planning, scheduling, and transportation has expertise in solving a wide range of practical planning problems, including large-scale network problems, logistics, and planning in the context of smart cities.
Students and staff of the Natural Language Processing Centre (NLP) have created many tools for processing texts and their enormous collections over more than 25 years of history. Many of these are used in workplaces around the world. Recently, the center's focus has shifted to large language models (LLMs) – creating models, fine-tuning them for specific tasks, or creating applications that use the models. In doing so, it continues to build on its expertise in the efficient processing of large text data, which is important for LLMs.